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Communication Dans Un Congrès Année : 2016

Lexicographic refinements in possibilistic Markov decision processes: the finite horizon case (LFA 2016)

Résumé

Possibilistic decision theory has been proposed twenty years ago and has had several extensions since then. Even though ap-pealing for its ability to handle qualitative decision problems, possibilistic decision theory suffers from an important drawback. Qualitative possibilistic utility criteria compare acts through min and max operators, which leads to a drowning effect. To over-come this lack of decision power of the theory, several refinements have been proposed. Lexicographic refinements are particularly appealing since they allow to benefit from the Expected Utility background, while remaining qualitative. This article aims at extend-ing lexicographic refinements to sequential decision problems i.e., to possibilistic decision trees and possibilistic Markov decision processes, when the horizon is finite. We present two criteria that refine qualitative possibilistic utilities and provide dynamic programming algorithms for calculating lexicographically optimal policies.
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Dates et versions

hal-03172234 , version 1 (17-03-2021)

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  • HAL Id : hal-03172234 , version 1

Citer

Nahla Ben Amor, Zeineb El Khalfi, Hélène Fargier, Régis Sabbadin. Lexicographic refinements in possibilistic Markov decision processes: the finite horizon case (LFA 2016). 25èmes Rencontres Francophones sur la Logique Floue et ses Applications (LFA 2016), Nov 2016, La Rochelle, France. pp.9.3. ⟨hal-03172234⟩
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